Reliable recognition of microaneurysms is an essential task when designing an automated analysis system for digital colour fundus images. In this work, we propose an integrated approach for automated microaneurysm detection with high accuracy.
Diabetic retinopathy (DR) is the most common microvascular complication of diabetes and remains the leading cause of vision loss in the working-age population. Early diagnosis through regular screening helps prevent vision loss. In a DR screening programme, a large number of digital retinal images need to be examined for the presence of DR by human experts. Pathological signs of DR in the digital colour fundus images are dark lesions including microaneurysms (MAs) and haemorrhages, as well as bright lesions such as exudates and cotton wool spots. FIG. 1 shows an instance of digital fundus image that contains both anatomic structures (Optic Disc, Macula and Blood Vessels) and pathological signs of DR (MAs, haemorrhage, and exudates). White boxes indicate some of the MAs.
An automated system for separating healthy and diseased regions in the image can efficiently reduce the workload associated with large scale screening. Over the last two decades, research in DR image analysis has been constantly increasing. Studies of automated DR screening systems have appeared in the literature. One critical stage in these automated image processing systems is microaneurysm detection.
MAs are the first visible signs of DR and they appear as small circular reddish dots in the retina. The quantity of MAs indicates the progression of DR. The complexity of MA recognition lies in the fact that MAs can be located anywhere in the retina: in isolation, in clusters, close to vasculature, around macula or among exudates. Meanwhile, their local contrast is very low compared to their surrounding background and their edges are not well defined in the image.
In addition, MAs have very similar intensity and morphological characteristics to other DR signs and anatomical features such as haemorrhages, thin vessel junctions, visually disconnected vessel fragments, local darkenings on the vessels or retinal background noise. FIGS. 2a to 2c show examples of some challenging cases for detecting MAs. Here, FIG. 2a shows a subtle MA, FIG. 2b shows an MA close to the vasculature, and FIG. 2c shows the vessel crossing that is similar to MAs.
Retinal images of patients from different ethnic groups also pose challenges for MA detection by varying background colour, introducing new disease patterns and often new non-DR diseases that are unknown to the automated system. FIG. 3 shows the retinal images from different populations. The first row shows the retinal images from different populations: (a) Kenya, (b) Botswana, (c) Mongolia, (d) China, (e) Saudi Arabia, (f) Italy, (g), Lithuania and (h) Norway. The second row shows the corresponding detailed subimages in the white boxes in the first row. Note that the images shown here were preprocessed by removing the black borders around the field of view. All images have been scaled to equal height for display.
Therefore, the present invention provides an automated system to recognise MAs in large scale fundus images with clinically acceptable accuracy regardless of their quality, ethnic origins and the type of camera used.
One exemplary prior art method applied a template-matching method to detect MAs in the wavelet domain. The authors considered that the wavelets could be effectively used to distinguish between lesions and non-lesion areas. The MAs were modelled with a 2D rotation-symmetric generalised Gaussian function, and the optimal adapted wavelet transform for MA detection was obtained by applying the lifting scheme framework. MAs were validated by applying a threshold on the matching result. The method of the present invention is focused on analysing the intensity profiles of MAs.
An intensity profile is a sequence of pixel intensities when scanning an image along certain direction. As the intensity profiles across MA objects have local minima, they can be modelled as an inverted 2D Gaussian shape. An understanding of the intensity profiles of an MA in an image plays an important role for an effective separation between the MA and other similar objects. A few methods following this approach for MA detection have been proposed by several authors. In one exemplary work, the cross-section profiles were extracted by detecting the local maximum pixels in an inverted image. A naïve Bayes (NB) classification was then used to remove false MA candidates based on a set of statistical measures of cross-section profiles, including the size, height and shape of the directional cross-sections of MAs.
Although these reported MA extraction approaches have some advantages, they still have difficulty in extracting MAs that are located close to blood vessels and discriminating MAs from the most common interfering structures such as vessel crossings and elongated haemorrhages.